Temporal Reasoning and Planning in Medicine Temporal Reasoning In Medical Information Systems (I) Yuval Shahar M.D., Ph.D.

Time as Symbolic Tokens “Chest pain, substernal, lasting less than 20 minutes” (the INTERNIST-I system for internal-medicine diagnosis [Miller et al., 1982]) –No ability to note [at least a potential] contradiction with “Chest pain, substernal, lasting more than 20 minutes” “Significant organisms isolated within the past 30 days” (The MYCIN system for infectious-diseases diagnosis and therapy) Answers are always Yes or No (Boolean predicates) and no further reasoning on the structure of the token is possible Typically, no transaction or valid times, only a duration

Fagan’s Ventilator Management (VM) System (Fagan, 1980) Designed for management of patients on ventilators in ICUs Rule based; rules were specific to contexts (states) Explicit reasoning about time units, rates of change Context-specific classifications mapped parameters into a context-sensitive range of values (e.g., acceptable), enabling context-free rules and context-free aggregation of abstractions Expiration dates of parameters represented in a good-for slot => constants, continuous, volunteered, deduced parameter types Special state-change rules inferred a new context (mode) Data could not be accepted out of temporal order (no valid time)

Blum’s Rx System (1982) Analyzed time-oriented clinical databases to produce a set of possible causal relations Used two modules in succession: –A Discovery Module, for automated discovery of statistical relations –A Study Module, to rule out spurious correlations, by using a medical knowledge base and by creating and testing a statistical model of the hypothesis Applied to data in the Stanford American Rheumatic Association Medical Information System (ARAMIS), which evolved from the Time Oriented Database (TOD) ARAMIS and TOD are three-dimensional historical databases –a patient ID indexes

Data Representation in Rx Point Events (e.g., laboratory test) Interval Events (e.g., a disease spanning several visits) –required an extension to the TOD Used a hierarchical derivation tree: Event A can be defined in terms of events B 1, B 2 ; these can be defined by C 11, C 12, and C 21, C 22 … –to assess a value, traverse its derivation tree and collect all values Sometimes a clinical parameter needs to be assessed when not measured (a latent variable), using proxy variables Time-dependent database access functions (delayed_action, delayed_effect, previous_value) Parameter-specific knowledge determined inter-episode gaps

Downs’ Medical-record Summarization Program (1986) Designed specifically to automate the summarization of online medical records Like Rx, used the Stanford ARAMIS database A knowledge base represented two classes of parameters: –abnormal attributes (abnormal findings) –derived attributes (including diseases that can be inferred from data) Each abnormal attribute points to a list of derived attributes that should be considered if its value is True Inference based on a hypothetico-deductive approach: Data-driven hypothesis generation produces a list of potential diagnoses; discrimination among competing hypotheses follows, using positive and negative evidence Probabilistic, Bayesian reasoning: Prior likelihood ratios are updated by each relevant datum Temporal predicates used, such as “the last 5 creatinine values are above 2.0”

Down’s Summarization Program’s Knowledge Base

De Zegher-Geets’ IDEFIX System (1987) Goal: Intelligent summarization of online patient records –Explain, for a given visit, all manifestations in that visit, given previous data Influenced by and extends Down’s summarization program Use the ARAMIS database, especially for systemic lupus erythematosus (SLE) patients Updated disease likelihoods by Bayesian odds-update functions Distinguished static medical knowledge from dynamic patient data Three levels of abstraction in the knowledge base’s ontology: – abnormal primary attributes (APAs) such as presence of protein in urine –abnormal states, such as nephrotic syndrome –diseases, such as SLE-related nephritis

The IDEFIX Inference Methods Two phase inference: –Goal-driven strategy explains given APAs, using a list of complications of the current disease –Followed by a data-driven strategy that tries to explain remaining APAs using a Cover and Differentiate approach based on odds- likelihood ratios, similar to Downs’ program Unlike Downs’ program, used severity levels using severity fiunctions for manifestations as well as for states or diseases Time-Oriented Probabilistic Functions (TOPFs) returned conditional probability of disease D given manifestation M as a function of a time interval (e.g., duration of D) –Used only to compute positive evidence; did not use context, severity

A Time-Oriented Probabilistic Function (TOPF)

Russ's Temporal Control Structure [TCS] System ( ) Manages data dependencies over time Data driven Supports decomposition of reasoning into static and dynamic parts Performs the necessary bookkeeping needed to ensure propagation of information in the system as well as completeness in processing A point-based approach with exact dating Performs procedural abstractions Supports hindsight as more data accumulate Evaluated as part of a diabetic ketoacidosis monitoring system

The TCS Environment

Partitioning the Database by TCS

A Process-Chain in TCS